Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject’s signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.

Wavelet transform profilometry is a three‐dimensional (3D) reconstruction method based on the structured light technique of fringe pattern projection, widely used because it is a non‐invasive, high‐performance 3D reconstruction method. The presence of shadows created by the object in the image capture process is an obstacle in obtaining accurate 3D reconstructions, as they add noise to the phase data, leading to artefacts in object reconstruction, even when using robust phase‐unwrapping algorithms. Since shadows present diverse intensities and shapes, detecting and eliminating their effects are challenging tasks. This work presents a novel method to detect shadow regions and reduce their effects in 3D reconstruction. The proposed method uses coloured fringe patterns to detect the shadows and mathematical morphology to condition the outlines of the shadow regions. The shadow outline information is used to interpolate the background‐plane fringe pattern onto the captured scene, where the shadows are detected. The mean squared error (MSE) of the reconstructed objects is reduced to 25% of the MSE without shadow removal, on an average, when using the Bioucas phase‐unwrapping method. When using the Ghiglia phase‐unwrapping method, the MSE reduction is to 8.3%, on an average, of the MSE in the shadow case.

The evaluation of the Resistance Spot Welding (RSW) that guarantees satisfactory performance of mechanical characteristics without altering physical properties can be reached by modeling the input parameters such as current, welding time, and applied force from which each unit has been built and correlating with digital images of the surface and infrared images that allows to identify variations on the parameters that modify the quality of the welding spot [1]. With this, mechanical and surface characteristics can be detected without the need for a mechanical test that modifies the structure of the unit. The database serves as a comprehensive record of the welding spot process, including the monitor of crucial input parameters such as current and force. The constructions and documentation of the testing platform through the instrumentation of a resistance welding will assess the variability of the input parameters and their impact on the output in surface and thermographic imaging, welding nugget diameter and it's mechanical strength. Additionally, it documents characteristics of the material used as thickness and material type and its output as the mechanical resistance and nugget diameter, along with its corresponding classification. Thus, the database not only captures the details of the welding process, but it also provides a valuable resource for analyzing and evaluating the performance of the welding operation.

Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder–Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.

Quality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive quality-evaluation methodology. The methodology connects the welding input and during-process parameters with the output visual quality information. A manual resistance spot welding machine was used to monitor and record the process input and output parameters to generate the dataset for training. The welding current, welding time, and electrode pressure data were correlated with the welding spot nugget’s quality, mechanical characteristics, and thermal and visible images. Six machine learning models were trained on visible and thermographic images to classify the weld’s quality and connect the quality characteristics (pull force and welding diameter) and the manufacturing process parameters with the visible and thermographic images of the weld. Finally, a cross-validation method validated the robustness of these models. The results indicate that the welding time and the angle between electrodes are highly influential parameters on the mechanical strength of the joint. Additionally, models using visible images of the welding spot exhibited superior performance compared to thermal images.

Particulate matter with a diameter less than 10 micrometers (PM10) is today an important subject of study, mainly because of its increasing concentration and its impact on environment and public health. This article summarizes the usage of convolutional neural networks (CNNs) to forecast PM10 concentrations based on atmospheric variables. In this particular case-study, the use of deep convolutional neural networks (both 1D and 2D) was explored to probe the feasibility of these techniques in prediction tasks. Furthermore, in this contribution, an ensemble method called Bagging (BEM) is used to improve the accuracy of the prediction model. Lastly, a well-known technique for PM10 forecasting, called multilayer perceptron (MLP) is used as a comparison to show the feasibility, accuracy, and robustness of the proposed model. In this contribution, it was found that the CNNs outperforms MLP, especially when they are executed using ensemble models.